As the dimensionality of the input samples is still too high for the anomaly detection task, we compare two methods for the feature space dimensionality reduction. As the first method, we reduce the number of input features to 50 with the PCA algorithm and then to 2 with the t-SNE method as suggested in [1]}. The second method we used was the reconstruction error metrics based feature space. For this purpose, we chose to use the L2 metric together with the SSIM suggested in [2]} in order to create a two dimensional feature space.
